Multicentre Evaluation of AI-Assisted Caries Detection on Panoramic Radiographs Among Early-Career Dentists

INTRODUCTION AND AIMS: Artificial intelligence - assisted diagnostic tools are increasingly introduced into dental practice, yet their impact on clinician performance in routine panoramic radiograph interpretation remains incompletely defined. This multicentre reader study evaluated whether AI assistance improves diagnostic accuracy, efficiency, and inter-reader consistency in caries detection, particularly among early-career dentists. METHODS: Twelve early-career dentists (≤3 years' experience) and three senior dentists (>10 years' experience) independently interpreted 402 anonymized panoramic radiographs under four conditions: unassisted reading, AI-assisted reading, AI-only analysis, and expert reference. Diagnostic performance was compared with a standardized expert-derived reference standard established by three experienced dentists using pixel-level annotations. Sensitivity, specificity, area under the receiver operating characteristic curve, interpretation time, and inter-reader agreement were analysed. RESULTS: AI assistance increased tooth-level sensitivity (82.4% vs 67.4%, P < .001) without compromising specificity (97.4% vs 97.2%), and reduced mean interpretation time (50.37 vs 65.12 seconds, P = .003). Case-level sensitivity improved from 84.7% to 93.3% (P < .001). Inter-reader agreement increased from κ = 0.61 to 0.73. The standalone AI system achieved a sensitivity of 79.2% (95% CI, 76.0-82.4) and specificity of 98.4% (95% CI, 76.0-82.4), and AUC of 0.938 (95% CI, 0.934-0.941). CONCLUSION: AI assistance improved diagnostic sensitivity, efficiency, and consistency among early-career dentists interpreting panoramic radiographs for caries detection, without increasing false-positive rates. CLINICAL RELEVANCE: AI-supported interpretation may help reduce experience-related variability in panoramic radiograph assessment and improve diagnostic efficiency in routine dental practice, particularly in settings with limited access to senior supervision.

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Journal
International Dental Journal
Published
2026-09-08
DOI
https://doi.org/10.1016/j.identj.2026.109812
Primary Topic
Dental Radiography and Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Multicentre Evaluation of AI-Assisted Caries Detection on Panoramic Radiographs Among Early-Career Dentists

Yujia Wu, 徐子能, Hailong Bai, Xuliang Deng et al.
International Dental Journal
Dental Radiography and Imaging
article

Multicentre Evaluation of AI-Assisted Caries Detection on Panoramic Radiographs Among Early-Career Dentists

Yujia Wu, 徐子能, Hailong Bai, Xuliang Deng, Mingming Xu, Xiaowei Hou, Yan Liu, Lili Chen, Peng Ding
article en

Abstract

INTRODUCTION AND AIMS: Artificial intelligence - assisted diagnostic tools are increasingly introduced into dental practice, yet their impact on clinician performance in routine panoramic radiograph interpretation remains incompletely defined. This multicentre reader study evaluated whether AI assistance improves diagnostic accuracy, efficiency, and inter-reader consistency in caries detection, particularly among early-career dentists. METHODS: Twelve early-career dentists (≤3 years' experience) and three senior dentists (>10 years' experience) independently interpreted 402 anonymized panoramic radiographs under four conditions: unassisted reading, AI-assisted reading, AI-only analysis, and expert reference. Diagnostic performance was compared with a standardized expert-derived reference standard established by three experienced dentists using pixel-level annotations. Sensitivity, specificity, area under the receiver operating characteristic curve, interpretation time, and inter-reader agreement were analysed. RESULTS: AI assistance increased tooth-level sensitivity (82.4% vs 67.4%, P < .001) without compromising specificity (97.4% vs 97.2%), and reduced mean interpretation time (50.37 vs 65.12 seconds, P = .003). Case-level sensitivity improved from 84.7% to 93.3% (P < .001). Inter-reader agreement increased from κ = 0.61 to 0.73. The standalone AI system achieved a sensitivity of 79.2% (95% CI, 76.0-82.4) and specificity of 98.4% (95% CI, 76.0-82.4), and AUC of 0.938 (95% CI, 0.934-0.941). CONCLUSION: AI assistance improved diagnostic sensitivity, efficiency, and consistency among early-career dentists interpreting panoramic radiographs for caries detection, without increasing false-positive rates. CLINICAL RELEVANCE: AI-supported interpretation may help reduce experience-related variability in panoramic radiograph assessment and improve diagnostic efficiency in routine dental practice, particularly in settings with limited access to senior supervision.

International Dental JournalVol. 76(6)
Hebei Medical University (CN), National Clinical Research (US), Stomatology Hospital (CN)
Beijing Municipal Science and Technology Commission
Openalex Percentile: Top 9%
Dental Radiography and Imaging
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